These days, whenever someone mentions \u201cAI that translates by itself,\u201d reactions are split: is there more excitement or more fear? The provocative title \u201cWho\u2019s Afraid of AI?\u201d aims to spark reflection on what is truly at stake\u2014beyond the illusion of \u201cfree and perfect translation.\u201d<\/p>\n
But these opportunities hold only if AI is\u00a0not<\/strong>\u00a0used \u201cblindly.\u201d That\u2019s where the risks come in.<\/p>\n Here are the critical pitfalls when AI is deployed without guardrails:<\/p>\n On October 6, 2025, it became public that Deloitte Australia would partially refund the Australian government after a 237-page report (AUD 440,000) contained serious errors \u2014 including fabricated quotes, false academic citations. This example is highly illustrative for potential clients: we are not talking about hypothetical fears, but real reputational and financial stakes.<\/p>\n To ensure AI works\u00a0for you<\/strong>, not\u00a0against you<\/strong>, here are recommended guidelines (and practices we at InnovaLang endorse):<\/p>\n \n In short: AI is a powerful enabler, but\u00a0not autonomous<\/strong>\u00a0when it comes to content that matters.<\/p>\n\n
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\nAI may produce citations, sources, facts that are fabricated but presented as truth. This phenomenon is known as \u201challucination\u201d in language models.
\n\u27a4 That\u2019s exactly what happened with the Deloitte report: fabricated citations and nonexistent academic sources, plus a quote invented from a court judgment.<\/li>\n
\nAI struggles with sarcasm, double meanings, idioms, cultural references, brand tone.
\nIn technical, legal, or scientific texts, a poorly translated term or ambiguous phrasing can dramatically shift meaning, with serious consequences.<\/li>\n
\nAI models are trained on large corpora containing cultural biases, stereotypes related to gender, class, ethnicity. These biases may surface in translation, e.g. by assigning gendered roles or choosing biased phrasing.<\/li>\n
\nUploading sensitive documents to AI systems (especially free or public cloud ones) carries risks that the data may be stored, reused, or used in training.
\nIn legal, medical, contractual domains, such leaks can amount to legal liability or regulatory violations.<\/li>\n
\nOver-automation may erode the critical oversight and human sensibility required to maintain style, tone, cultural resonance. Over time, content becomes flat, generic, bland.<\/li>\n
\nA wrongly translated press release, contract clause, or website text published publicly can have high visibility. The trust of stakeholders, clients, partners may erode. In extreme cases, contractual or legal liability may arise.<\/li>\n<\/ol>\n\n
\nThe original report has since been revised; Deloitte stated that AI usage did not alter the core findings, but acknowledged errors in citations and references.
\nThis high-profile incident shows that even large, organized firms with review processes may misstep if they treat AI as a magic black box.<\/p>\n\n
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\n Phase<\/strong><\/td>\n Recommendation<\/strong><\/td>\n<\/tr>\n<\/thead>\n\n \n Risk assessment<\/strong><\/td>\n Classify text by criticality: low (FAQ, generic), medium (marketing, website) or high (legal, contracts, regulatory).<\/td>\n<\/tr>\n \n Technology selection<\/strong><\/td>\n Use professional, customizable models with glossaries, term memory, not generic free tools lacking confidentiality guarantees.<\/td>\n<\/tr>\n \n Hybrid AI + human<\/strong><\/td>\n Use AI only for draft stage; final version must be refined by experienced human translators.<\/td>\n<\/tr>\n \n Contextual verification<\/strong><\/td>\n Cross-check references, citations, factual consistency, coherence, terminological consistency.<\/td>\n<\/tr>\n \n Glossaries & terminology memory<\/strong><\/td>\n Embed client lexicons or terminologies to constrain AI to brand tone and consistent vocabulary.<\/td>\n<\/tr>\n \n Final quality control<\/strong><\/td>\n Human checklist: terminological correctness, coherence, contextual sense, legal consistency, sources.<\/td>\n<\/tr>\n \n Data governance & confidentiality policies<\/strong><\/td>\n Clear NDAs, confidentiality clauses, protocols for data handling, assurances of non-reuse for training.<\/td>\n<\/tr>\n \n Post-publication monitoring<\/strong><\/td>\n Collect user feedback, track flagged errors, and update internal processes accordingly.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n